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Review on Teacher's Classroom Language Behavior Analysis based on Clustering and Emotional Analysis

Review on Teacher's Classroom Language Behavior Analysis Bas...

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international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Lu, Lingling Yuan, Hao Yang, Shuya Feng, Lezhou Ding, Xiaoming Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789819975556

With the rapid development of deep learning technology, its applications in various fields are also increasing. In addition to making gratifying progress in traditional image classification, speech recognition, text classification, and other fields, it has also begun to play an important role in more specific and professional research scenarios, such as applying it to specific work such as fault detection in the power industry and case text analysis in the public security field, Fully utilize its self-learning and self-improvement characteristics and functions to provide assistance for the implementation of practical work. based on the characteristics of deep learning technology, this article starts with the study of classroom teaching behavior analysis in the field of teaching analysis, and explores the feasibility of applying deep learning technology to classroom teaching behavior analysis research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault detection

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Efficient Low Light Video Enhancement based on Improved Retinex Algorithms

Efficient Low Light Video Enhancement Based on Improved Reti...

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2023 IEEE international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Lee, Sung-Ling Yang, Shih-Hsuan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350313154

Videos shot in low-light environments suffer from low contrast and high noise. In this paper, an improved zero-reference low-light enhancement technique for videos based on the Retinex model is presented. The proposed method improves the existing Retinex approaches in several aspects. First, the image features extracted by the VGG network are employed as a part of the input to the generator of the Retinex parameters for increasing temporal stability. Second, a deformable convolution kernel is used to enhance the spatial correlation. Third, the optical flow between frames is approximated as a combination of affine linear transformations for reducing complexity. Compared with the state-of-the-art low-light enhancement algorithms, the proposed method achieves more favorable and stable image qualities in PSNR and SSIM with short processing time. © 2023 IEEE.

关键词： Linear transformations

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2nd international conference on Advances in Information and Communication technology, ICTA 2023

2nd International Conference on Advances in Information and ...

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2nd international conference on Advances in Information and Communication technology, ICTA 2023

ISBN: (纸本)9783031508172

The proceedings contain 35 papers. The special focus in this conference is on Advances in Information and Communication technology. The topics include: A Real-time Crowd Density Level Detection System;an Architecture for More Fine-Grained Hidden Representation in Named Entity Recognition for Biomedical Texts;application of Deep Learning Methods for Forest Fire Intelligent image Processing;choosing Data Points to Label for Semi-supervised Learning based on Neighborhood;classification of Fermentation Levels of Cacao Beans (Theobroma cacao L.) Using Sensing technology and Neural Networks;DCDynUnet: Deep Supervision Attention Context for Brain Segmentation;enhancing Wildfire Detection Using Semi-supervised Fuzzy Clustering on Satellite imagery;Evaluating the Performance of Some Deep Learning Model for the Problem of Emotion Recognition based on EEG signal;a Novel Arm Bone Fracture Detection Using Deep Learning;feature Reduction for Interpretability of Neuro-Fuzzy Classifier;improved Accuracy of Path System on Creating Intelligence Base;the Vehicle Routing Problem with Drones for Fresh Agricultural Products;ViT-SigNet: Combining Deep CNN and Vision Transformer for Enhanced Signature Verification;combining Local Search and Multi-objective Optimization Algorithm in signalized Intersection Optimization;Comparative Evaluation of PRR and PODA Methods for Model Order Reduction in Electrical Circuits;data-Driven Narratives: Unleashing the Potential of R for Journalistic Storytelling;dynamic Analysis of Capsubot Model in Liquid Environment by Numerical Method;genetic Programming–A Preliminary Study of Knowledge Transfer in Mutation;improvement of Spectral Clustering Method in Social Network Community Detection;a Systematic Review of Artificial Intelligence in Geographic Information systems;probable Characteristics of Multi-channel Queuing systems with "Impatient" and "Patient" Claims.

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A Study on Deep Learning-based image Target Identification Techniques 5th

A Study on Deep Learning-Based Image Target Identification T...

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5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Wang, Yining College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9789819751808

This study aims to explore deep learning-based image target recognition methods to improve the performance of target detection and classification in the field of computer vision. The experiments use satellite-acquired remote sensing images of moving objects, including four target categories such as buses, cars, bicycles and ships. In model training, we performed parameter optimisation by stochastic gradient descent algorithm and set the training period to 12. To validate the model’s effectiveness, we compare the improved R-CNN (Region-based Convolutional Neural Network), traditional CNN (Convolutional Neural Network) and Faster-RCNN (Faster Region-based Convolutional Neural Network) models, and generate confusion matrices to evaluate their performance. The experimental results show that the improved model exhibits excellent performance in recognition of different types of target objects, especially for larger targets. This research provides reliable experimental support for the application of deep learning in the field of image target recognition and is expected to promote further development and innovation in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A convolutional neural network based on optimized structure and its lightweighting 2

A convolutional neural network based on optimized structure ...

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2nd international conference on Advanced Algorithms and signal image Processing, AASIP 2022

作者： Deng, Jinyong Zhao, Zhiheng Liu, Yang Chen, Yongzhe Zhang, Zhefan Zhang, Yu School of Electrical Engineering & Automation Harbin Institute of Technology Harbin15001 China

ISBN: (纸本)9781510660571

In this paper, we design a convolutional neural network based on the ideas of depthwise separable convolution and inverted residual module. The scaling factor of BN layer is used as a measure for channel pruning of the network model to compress it. By analyzing the layer-by-layer pruning process of conventional convolution, the layer-by-layer pruning method with depthwise separable convolution and inverted residual structure is proposed to prune the channels of the network model, and finally, the channel pruning strategy of classification simplification network is developed. Tests on the selected dataset showed that the classification accuracy of the pruned and fine-tuned network model is 97.7% when the pruning rate is 0.7. © 2022 SPIE.

关键词： Convolution

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Combined Wavelet and multiwavelet-based Convolutional Neural Network (CWM-CNN) for Remote Sensing image Super Resolution

Combined Wavelet and multiwavelet-based Convolutional Neural...

引用

international conference on Computer Vision and internet of Things 2023, ICCVIoT 2023

作者： Sandana, Sudaroli J. Thool, Kunal Vijay Deivalakshmi, S. Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9781839539848

Acquiring remote sensing (RS) images with high pixel resolution quickly and easily in geographic information systems is extremely important. Existing Convolutional Neural Network (CNN) models generally construct a network with a massive number of layers and parameters, resulting in high computational complexity and considerable memory consumption. To overcome this issue, a Combined Wavelet and Multiwavelet Transform based Convolutional Neural Network (CWM-CNN) by fusing the properties of discrete wavelet transform (DWT) and multiwavelet transform (MWT) with smaller number of convolutional layers is proposed. The primary concept of the proposed technique is to combine Wavelet and Multiwavelet Transformed images for enhancing the resolution of the RS images. Wavelet and multiwavelet transforms are employed in the proposed work to extract shallow features from the input image, and a feature extraction block (FEB) with a compact 3-layer CNN is designed to extract deep features to improve image resolution. Three different RS image datasets, including the NW-VHU dataset, the AID Dataset, and the UCMerced Benchmark Dataset, are utilized to analyze the outcome of the proposed method. The results reveal that the proposed method outperforms other current models in terms of performance while using significantly less parameters. © The Institution of Engineering & technology 2023.

关键词： Discrete wavelet transforms

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A Link Prediction-based Routing Maintenance Algorithm for Ad-Hoc Networks in Urban Rail Transit Scenarios 8

A Link Prediction-Based Routing Maintenance Algorithm for Ad...

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8th international conference on Communication, image and signal Processing, CCISP 2023

作者： Cai, Shiyuan Cai, Yuchen Wang, Wei Liu, Liu Zhang, Ju Su, Zhaoyang Liu, Qi School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Traffic Control Technology Co. Traffic Control Technology Co. Ltd Beijing China Smart City Research Institute China Unicom Beijing China

ISBN: (纸本)9798350305838

The application of wireless Ad-Hoc network technology in urban rail transit can improve the performance of urban rail transit vehicle-ground communication systems and is more conducive to meeting the demand of next-generation urban rail transit vehicle-ground communication. This paper focuses on the routing maintenance mechanism of urban rail transit Ad-Hoc network, and introduces a link prediction-based routing maintenance mechanism for the characteristics of trains moving at high speed in urban rail transit Ad-Hoc network, so as to avoid the problem of frequent link breakage when trains are moving forward, which leads to data transmission performance degradation. The relevant simulation results show that the proposed routing maintenance mechanism for Ad-Hoc networks of urban rail transit can provide more efficient data transmission methods while reducing the routing overhead in the network. © 2023 IEEE.

关键词： Maintenance

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Intelligent Traffic Light System based on IoT 2

Intelligent Traffic Light System based on IoT

引用

2nd international conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Gupta, Rohan Singh, Ankit Kumar Tewari, Pratistha Pankaj Dabral, Suhani Dept. of ECE UIE Chandigarh University Mohali India Dept. of CSE UIE Chandigarh University Mohali India

ISBN: (纸本)9798350340235

With rise in urbanization there has been an increase in traffic density, resulting in various traffic related issues like traffic congestion and increased air pollution. Hence traffic issues are one of the biggest issues faced by the people indaily life and needs to be handled by the government andadministration in an effective, practical and smart way. In smarttimes when internet has solution for most of the human problems, thenwhy not use IoT to solve road traffic problems. The Intelligent Traffic Light system based on IoT and traffic density is a modern approach towards existing traffic system which works on real time data analysis using ultrasonic sensor and gas *** technology is intended to handle traffic congestion by automatically modifying traffic signal timings at each interaction based on traffic density. Its goal is to increase road safety, minimize traffic jams, and improve overall traffic flow. A smart method for the emerging smart world. The unique partof the proposed system is the addition of gas sensor which will measure the level of air pollutants and accordingly with ultrasonic sensor which measures density of traffic to reduce traffic congestion and improve air quality. © 2023 IEEE.

关键词： gas sensor internet of Things real time data analysis traffic density ultrasonic sensor

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An image encryption algorithm based on multiple chaotic systems and DNA encoding 14

An image encryption algorithm based on multiple chaotic syst...

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14th international conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Jiang, Feng-Bai Zhang, Xue-Feng Jiang, Wen School of Cyberspace Security Xi'an University of Posts and Telecommunications Xi'an710121 China National Internet Emergency Center Beijing100029 China

ISBN: (纸本)9781665499781

Aiming at the problems of image encryption algorithm in terms of security and encryption effect, an image encryption algorithm based on multiple chaotic maps and DNA encoding is proposed. First, using Logistic map to randomly scramble the rows and columns of the plaintext image, and convert the result into a binary matrix;second, according to the DNA coding rules, convert the binary matrix into a DNA matrix, and use 2 D - L S M M map and Logistic-Sine system to perform in DNA encoding operation, X O R operation is performed on the obtained encoding matrix to obtain an encrypted DNA matrix;finally, according to the DNA decoding rules, the encrypted DNA matrix is converted into a digital image to obtain a ciphertext image. Experimental simulation and performance analysis show that the algorithm has better encryption and decryption effects, larger key space, and higher security and robustness. © 2022 IEEE

关键词： Chaotic systems

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Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup 27

Implementation of AKKF-based Multi-Sensor Fusion Methods in ...

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27th international conference on Information Fusion (FUSION)

作者： Wright, James S. Sun, Mengwei Davies, Mike E. Proudler, Ian K. Hopgood, James R. Dstl Dstl Porton Bldg 5Rm 101 Salisbury SP4 0JQ Wilts England Cranfield Univ Sch Aerosp Transport & Mfg SATM Coll Rd Cranfield MK43 OAL Beds England Univ Edinburgh Inst Imaging Data & Commun Edinburgh EH9 3FG Midlothian Scotland Univ Strathclyde Ctr Signal & Image Proc CeSIP Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

ISBN: (纸本)9798350371420;9781737749769

This paper explores the increasing demand for accurate and resilient multi-sensor fusion techniques, particularly within 3D tracking systems enhanced by drone technology. Employing the adaptive kernel Kalman filter (AKKF) methodology within the Stone Soup framework, our research seeks to develop robust fusion approaches capable of seamlessly amalgamating data from a multi-sensor arrangement with fixed ground sensors and dynamic sensors mounted on drones. By capitalising on the adaptive nature of the AKKF, we aim to refine the precision and dependability of 3D object tracking in intricate scenarios. Through empirical evaluations, we illustrate the effectiveness of our proposed AKKF-based fusion strategies in enhancing tracking performance within the Stone Soup framework, thus contributing to the advancement of multi-sensor fusion methodologies within this framework.

关键词： Adaptive kernel Kalman filter sensor fusion Stone Soup 3D Tracking

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